用于水下导航的最大熵多项式混沌卡尔曼滤波器

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-09-12 DOI:10.1016/j.dsp.2024.104774
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引用次数: 0

摘要

本文开发了一种水下导航解决方案,它利用带式惯性导航系统(SINS),并融合了一系列辅助传感器,如声学定位系统、多普勒速度记录仪、深度计和磁力计,以精确估计水下船只的位置和方向。传统的综合导航系统假定测量噪声为高斯噪声,而实际上噪声是非高斯噪声,特别是受到重尾脉冲噪声的污染。为解决这一问题,并将系统模型与获取的传感器测量值有效融合,我们开发了一种基于最大熵标准的平方根多项式混沌卡尔曼滤波器。所提出的方法使用 Hermite 多项式混沌扩展来解决非线性问题,在存在非高斯测量噪声的情况下,它有可能以更精确的方式估计状态。滤波器使用声学信标进行初始化,以准确定位车辆的初始位置。所提滤波器的计算复杂度是按触发次数计算的。在估计精度和计算复杂度方面,将所提出的方法与现有的最大熵西格玛点滤波器进行了比较。模拟结果表明,与传统的确定性采样点滤波器和 Huber 的 M-estimator 相比,所提出的方法更加精确。
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Maximum correntropy polynomial chaos Kalman filter for underwater navigation

This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The proposed method uses Hermite polynomial chaos expansion to tackle the nonlinearity, and it has the potential to estimate the states in a more accurate way in presence of a non-Gaussian measurement noise. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. It is found from the simulation results that the proposed method is more accurate compared to the conventional deterministic sample point filters and Huber's M-estimator.

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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
期刊最新文献
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